Search Results (27)

This study proposes a novel method that uses radar for markerless motion classification by using effective features derived from micro-Doppler signals. The training phase uses three-dimensional marker coordinates captured by a motion-capture system to construct basis functions, which enable modeling of micro-motions of the human body. During the testing phase, motion classification is performed without markers, relying solely on radar signals. The feature vectors are generated by applying cross-correlation between the received radar signal and the basis functions, then compressed using principal component analysis, and classified using a simple nearest-neighbor algorithm. The proposed method achieves nearly 100% classification accuracy with a compact feature set and is accurate even at high signal-to-noise ratios. Experimental results demonstrate that to optimize training data and increase computational efficiency, the sampling duration and sampling interval must be set appropriately. Full article

The aim of this pilot study was to investigate the feasibility of markerless tracking to assess upper body movements of children with and without human immunodeficiency virus encephalopathy (HIV-E). Sagittal and frontal video recordings were used to track anatomical landmarks with the DeepLabCut pre-trained human model in five children with HIV-E and five typically developing (TD) children to calculate shoulder flexion/extension, shoulder abduction/adduction, elbow flexion/extension and trunk lateral sway. Differences in joint angle trajectories of the two cohorts were investigated using a one-dimensional statistical parametric mapping method. Children with HIV-E showed a larger range of motion in shoulder abduction and trunk sway than TD children. In addition, they showed more shoulder extension and more lateral trunk sway compared to TD children. Markerless tracking was feasible for 2D movement analysis and sensitive to observe expected differences in upper limb and trunk sway movements between children with and without HIVE. Therefore, it could serve as a useful alternative in settings where expensive gait laboratory instruments are unavailable, for example, in clinical centers in low- to middle-income countries. Future research is needed to explore 3D markerless movement analysis systems and investigate the reliability and validity of these systems against the gold standard 3D marker-based systems that are currently used in clinical practice. Full article

(1) Background: Marker-based optical motion tracking is the gold standard in gait analysis; however, markerless solutions are rapidly emerging today. Algorithms like Openpose can track human movement from a video. Few studies have assessed the validity of this method. This study aimed to assess the reliability of Openpose in measuring the kinematics and spatiotemporal gait parameters. (2) Methods: This analysis used simultaneously recorded video and optoelectronic motion capture data. We assessed 20 subjects with different gait impairments (healthy, right hemiplegia, left hemiplegia, paraparesis). The two methods were compared using computing absolute errors (AEs), intraclass correlation coefficients (ICCs), and cross-correlation coefficients (CCs) for normalized gait cycle joint angles. (3) Results: The spatiotemporal parameters showed an ICC between good to excellent, and the absolute error was very small: cadence AE = 1.63 step/min, Mean Velocity AE = 0.16 m/s. The Range of Motion (ROM) showed a good to excellent agreement in the sagittal plane. Furthermore, the normalized gait cycle CCC values indicated moderate to strong coupling in the sagittal plane. (4) Conclusions: We found Openpose to be accurate for sagittal plane gait kinematics and for spatiotemporal gait parameters in the healthy and pathological subjects assessed. Full article

The assessment of running kinematics is essential for injury prevention and rehabilitation, including anterior cruciate ligament sprains. Recent advances in computer vision have enabled the development of tools for quantifying kinematics in research and clinical settings. This study evaluated the accuracy of an OpenPifPaf-based markerless method for assessing sagittal plane kinematics of the ankle, knee, and hip during treadmill running using smartphone video footage and examined the impact of clothing on the results. Thirty healthy participants ran at 2.5 and 3.6 m/s under two conditions: (1) wearing minimal clothing with markers to record kinematics by using both a smartphone and a marker-based system, and (2) wearing usual running clothes and recording kinematics by only using a smartphone. Joint angles, averaged over 20 cycles, were analysed using SPM1D and RMSE. The markerless method produced kinematic waveforms closely matching the marker-based results, with RMSEs of 5.6° (hip), 3.5° (ankle), and 2.9° (knee), despite some significant differences identified by SPM1D. Clothing had minimal impact, with RMSEs under 2.8° for all joints. These findings highlight the potential of the OpenPifPaf-based markerless method as an accessible, simple, and reliable tool for assessing running kinematics, even in natural attire, for research and clinical applications. Full article

Clinical gait analysis plays a central role in the rehabilitation of stroke patients. However, practical and technical challenges limit their use in clinical settings. This study aimed to validate SMARTGAIT, a deep learning-based gait analysis system that addresses these limitations. Eight stroke patients took part in the study at the Human Performance Research Centre of the University of Konstanz. Gait measurements were taken using both the marker-based Vicon motion capture system and the single-smartphone-based SMARTGAIT system. We evaluated the agreement for knee, hip, and ankle joint angle kinematics in the frontal and sagittal plane and spatiotemporal gait parameters between the two systems. The results mostly demonstrated high levels of agreement between the two systems, with Pearson correlations of ≥0.79 for all lower body angle kinematics in the sagittal plane and correlations of ≥0.71 in the frontal plane. RMSE values were ≤4.6°. The intraclass correlation coefficients for all derived gait parameters showed good to excellent levels of agreement. SMARTGAIT is a promising tool for gait analysis in stroke, particularly for quantifying gait characteristics in the sagittal plane, which is very relevant for clinical gait analysis. However, further analyses are required to validate the use of SMARTGAIT in larger samples and its transferability to different types of pathological gait. In conclusion, a single smartphone recording (monocular 2D RGB camera) could make gait analysis more accessible in clinical settings, potentially simplifying the process and making it more feasible for therapists and doctors to use in their day-to-day practice. Full article

Technological advancements have expanded the range of methods for capturing human body motion, including solutions involving inertial sensors (IMUs) and optical alternatives. However, the rising complexity and costs associated with commercial solutions have prompted the exploration of more cost-effective alternatives. This paper presents a markerless optical motion capture system using a RealSense depth camera and intelligent computer vision algorithms. It facilitates precise posture assessment, the real-time calculation of joint angles, and acquisition of subject-specific anthropometric data for gait analysis. The proposed system stands out for its simplicity and affordability in comparison to complex commercial solutions. The gathered data are stored in comma-separated value (CSV) files, simplifying subsequent analysis and data mining. Preliminary tests, conducted in controlled laboratory environments and employing a commercial MEMS-IMU system as a reference, revealed a maximum relative error of 7.6% in anthropometric measurements, with a maximum absolute error of 4.67 cm at average height. Stride length measurements showed a maximum relative error of 11.2%. Static joint angle tests had a maximum average error of 10.2%, while dynamic joint angle tests showed a maximum average error of 9.06%. The proposed optical system offers sufficient accuracy for potential application in areas such as rehabilitation, sports analysis, and entertainment. Full article

This study examined the efficacy of an optimized DeepLabCut (DLC) model in motion capture, with a particular focus on the sit-to-stand (STS) movement, which is crucial for assessing the functional capacity in elderly and postoperative patients. This research uniquely compared the performance of this optimized DLC model, which was trained using ’filtered’ estimates from the widely used OpenPose (OP) model, thereby emphasizing computational effectiveness, motion-tracking precision, and enhanced stability in data capture. Utilizing a combination of smartphone-captured videos and specifically curated datasets, our methodological approach included data preparation, keypoint annotation, and extensive model training, with an emphasis on the flow of the optimized model. The findings demonstrate the superiority of the optimized DLC model in various aspects. It exhibited not only higher computational efficiency, with reduced processing times, but also greater precision and consistency in motion tracking thanks to the stability brought about by the meticulous selection of the OP data. This precision is vital for developing accurate biomechanical models for clinical interventions. Moreover, this study revealed that the optimized DLC maintained higher average confidence levels across datasets, indicating more reliable and accurate detection capabilities compared with standalone OP. The clinical relevance of these findings is profound. The optimized DLC model’s efficiency and enhanced point estimation stability make it an invaluable tool in rehabilitation monitoring and patient assessments, potentially streamlining clinical workflows. This study suggests future research directions, including integrating the optimized DLC model with virtual reality environments for enhanced patient engagement and leveraging its improved data quality for predictive analytics in healthcare. Overall, the optimized DLC model emerged as a transformative tool for biomechanical analysis and physical rehabilitation, promising to enhance the quality of patient care and healthcare delivery efficiency. Full article

The use of motion-analysis devices that can measure the progress of rehabilitation exercises for nerve paralysis is increasing because of the need to confirm the effectiveness of treatment for sports injuries. This study developed a new motion-analysis device that can be easily handled compared with the existing VICON motion-analysis device. Motion analysis of the human body (specifically, hip flexion, knee flexion, and trunk rotation) performed simultaneously with the new device and the existing VICON device was compared. Five healthy young men voluntarily participated in this study. Various joint angles were captured using a marker-less multi-view image-based motion-analysis system and a VICON motion capture system with markers during lower-extremity work. Intra-class correlation coefficient (ICC) analysis was used to examine simultaneous- and angular-limit validity and the intra-joint reliability of multi-point image-based motion-analysis systems. Simultaneous validity analysis showed that the highest ICCs for hip flexion, knee flexion, and trunk rotation were 0.924–0.998, 0.842–0.989 or higher, and 0.795–0.962, respectively. We confirmed that this new marker-less motion-analysis system has high accuracy and reliability in measuring joint kinematics in the lower extremities during rehabilitation and in monitoring the performance of athletes in training facilities. Full article

Accuracy validation of gait analysis using pose estimation with artificial intelligence (AI) remains inadequate, particularly in objective assessments of absolute error and similarity of waveform patterns. This study aimed to clarify objective measures for absolute error and waveform pattern similarity in gait analysis using pose estimation AI (OpenPose). Additionally, we investigated the feasibility of simultaneous measuring both lower limbs using a single camera from one side. We compared motion analysis data from pose estimation AI using video footage that was synchronized with a three-dimensional motion analysis device. The comparisons involved mean absolute error (MAE) and the coefficient of multiple correlation (CMC) to compare the waveform pattern similarity. The MAE ranged from 2.3 to 3.1° on the camera side and from 3.1 to 4.1° on the opposite side, with slightly higher accuracy on the camera side. Moreover, the CMC ranged from 0.936 to 0.994 on the camera side and from 0.890 to 0.988 on the opposite side, indicating a “very good to excellent” waveform similarity. Gait analysis using a single camera revealed that the precision on both sides was sufficiently robust for clinical evaluation, while measurement accuracy was slightly superior on the camera side. Full article

The center of mass dynamics of the seated posture of humans in a work environment under hypogravity (0 < g < 1) have rarely been investigated, and such research is yet to be carried out. The present study determined the difference in the body system of 32 participants working under simulated 1/6 g (Moon) and 1 g (Earth) for comparison using static and dynamic task measurements. This was based on a markerless motion capture method that analyzed participants’ center of mass at the start, middle and end of the task when they began to get fatigued. According to this analysis, there is a positive relationship (p < 0.01) with a positive coefficient of correlation between the downward center of mass body shift along the proximodistal axis and gravity level for males and females. At the same time, the same positive relationship (p < 0.01) between the tilt of the body backward along the anterior–posterior axis and the level of gravity was found only in females. This offers fresh perspectives for comprehending hypogravity in a broader framework regarding its impact on musculoskeletal disorders. It can also improve workplace ergonomics, body stability, equipment design, and biomechanics. Full article

New technologies based on virtual reality and augmented reality offer promising perspectives in an attempt to increase the assessment of human kinematics. The aim of this work was to develop a markerless 3D motion analysis capture system (MOVA3D) and to test it versus Qualisys Track Manager (QTM). A digital camera was used to capture the data, and proprietary software capable of automatically inferring the joint centers in 3D and performing the angular kinematic calculations of interest was developed for such analysis. In the experiment, 10 subjects (22 to 50 years old), 5 men and 5 women, with a body mass index between 18.5 and 29.9 kg/m², performed squatting, hip flexion, and abduction movements, and both systems measured the hip abduction/adduction angle and hip flexion/extension, simultaneously. The mean value of the difference between the QTM system and the MOVA3D system for all frames for each joint angle was analyzed with Pearson’s correlation coefficient (r). The MOVA3D system reached good (above 0.75) or excellent (above 0.90) correlations in 6 out of 8 variables. The average error remained below 12° in only 20 out of 24 variables analyzed. The MOVA3D system is therefore promising for use in telerehabilitation or other applications where this level of error is acceptable. Future studies should continue to validate the MOVA3D as updated versions of their software are developed. Full article

Gait analysis is an essential tool for detecting biomechanical irregularities, designing personalized rehabilitation plans, and enhancing athletic performance. Currently, gait assessment depends on either visual observation, which lacks consistency between raters and requires clinical expertise, or instrumented evaluation, which is costly, invasive, time-consuming, and requires specialized equipment and trained personnel. Markerless gait analysis using 2D pose estimation techniques has emerged as a potential solution, but it still requires significant computational resources and human involvement, making it challenging to use. This research proposes an automated method for temporal gait analysis that employs the MediaPipe Pose, a low-computational-resource pose estimation model. The study validated this approach against the Vicon motion capture system to evaluate its reliability. The findings reveal that this approach demonstrates good (ICC_(2,1) > 0.75) to excellent (ICC_(2,1) > 0.90) agreement in all temporal gait parameters except for double support time (right leg switched to left leg) and swing time (right), which only exhibit a moderate (ICC_(2,1) > 0.50) agreement. Additionally, this approach produces temporal gait parameters with low mean absolute error. It will be useful in monitoring changes in gait and evaluating the effectiveness of interventions such as rehabilitation or training programs in the community. Full article

Gait analysis plays an important role in the fields of healthcare and sports sciences. Conventional gait analysis relies on costly equipment such as optical motion capture cameras and wearable sensors, some of which require trained assessors for data collection and processing. With the recent developments in computer vision and deep neural networks, using monocular RGB cameras for 3D human pose estimation has shown tremendous promise as a cost-effective and efficient solution for clinical gait analysis. In this paper, a markerless human pose technique is developed using motion captured by a consumer monocular camera (800 × 600 pixels and 30 FPS) for clinical gait analysis. The experimental results have shown that the proposed post-processing algorithm significantly improved the original human pose detection model (BlazePose)’s prediction performance compared to the gold-standard gait signals by 10.7% using the MoVi dataset. In addition, the predicted T² score has an excellent correlation with ground truth (r = 0.99 and y = 0.94x + 0.01 regression line), which supports that our approach can be a potential alternative to the conventional marker-based solution to assist the clinical gait assessment. Full article

Markerless estimation of 3D Kinematics has the great potential to clinically diagnose and monitor movement disorders without referrals to expensive motion capture labs; however, current approaches are limited by performing multiple de-coupled steps to estimate the kinematics of a person from videos. Most current techniques work in a multi-step approach by first detecting the pose of the body and then fitting a musculoskeletal model to the data for accurate kinematic estimation. Errors in training data of the pose detection algorithms, model scaling, as well the requirement of multiple cameras limit the use of these techniques in a clinical setting. Our goal is to pave the way toward fast, easily applicable and accurate 3D kinematic estimation. To this end, we propose a novel approach for direct 3D human kinematic estimation D3KE from videos using deep neural networks. Our experiments demonstrate that the proposed end-to-end training is robust and outperforms 2D and 3D markerless motion capture based kinematic estimation pipelines in terms of joint angles error by a large margin (35% from 5.44 to 3.54 degrees). We show that D3KE is superior to the multi-step approach and can run at video framerate speeds. This technology shows the potential for clinical analysis from mobile devices in the future. Full article

Parkinson’s disease (PD) is a neurodegenerative disorder that is more common in elderly people and affects motor control, flexibility, and how easily patients adapt to their walking environments. PD is progressive in nature, and if undetected and untreated, the symptoms grow worse over time. Fortunately, PD can be detected early using gait features since the loss of motor control results in gait impairment. In general, techniques for capturing gait can be categorized as computer-vision-based or sensor-based. Sensor-based techniques are mostly used in clinical gait analysis and are regarded as the gold standard for PD detection. The main limitation of using sensor-based gait capture is the associated high cost and the technical expertise required for setup. In addition, the subjects’ consciousness of worn sensors and being actively monitored may further impact their motor function. Recent advances in computer vision have enabled the tracking of body parts in videos in a markerless motion capture scenario via human pose estimation (HPE). Although markerless motion capture has been studied in comparison with gold-standard motion-capture techniques, it is yet to be evaluated in the prediction of neurological conditions such as PD. Hence, in this study, we extract PD-discriminative gait features from raw videos of subjects and demonstrate the potential of markerless motion capture for PD prediction. First, we perform HPE on the subjects using AlphaPose. Then, we extract and analyse eight features, from which five features are systematically selected, achieving up to 93% accuracy, 96% precision, and 92% recall in arbitrary views. Full article